code: def test_augmentation_param_fixed_model():
      hp = hp_module.HyperParameters()
      aug_model = keras.Sequential(
          [keras.layers.RandomRotation(1.0)], name="aug"
      )
      hypermodel = efficientnet.HyperEfficientNet(
          input_shape=INPUT_SHAPE_32,
          classes=10,
          augmentation_model=aug_model,
      )
      model = hypermodel.build(hp)
      assert model.layers[1].name == "aug"
  
file_path: keras-tuner/keras_tuner/applications/efficientnet_test.py
imports: import numpy
  import pytest
  from keras_tuner.applications import efficientnet
  from keras_tuner.backend import config
  from keras_tuner.backend import keras
  from keras_tuner.engine import hypermodel
  from keras_tuner.engine import hyperparameters
label_desc: Use image node_id 7 for calling a global function with example usage: test_augmentation_param_fixed_model() without return types